A powerful test of independent assortment that determines genome-wide significance quickly and accurately

In the analysis of DNA sequences on related individuals, most methods strive to incorporate as much information as possible, with little or no attention paid to the issue of statistical significance. For example, a modern workstation can easily handle the computations needed to perform a large-scale genome-wide inheritance-by-descent (IBD) scan, but accurate assessment of the significance of that scan is often hindered by inaccurate approximations and computationally intensive simulation. To address these issues, we developed gLOD-a test of co-segregation that, for large samples, models chromosome-specific IBD statistics as a collection of stationary Gaussian processes. With this simple model, the parametric bootstrap yields an accurate and rapid assessment of significance-the genome-wide corrected P-value. Furthermore, we show that (i) under the null hypothesis, the limiting distribution of the gLOD is the standard Gumbel distribution; (ii) our parametric bootstrap simulator is approxi...